\begin{abstract}
In recent years, artificial development has been introduced to evolutionary algorithms as a means to overcome the scalability problem. Though in its early stages, it has been showing a lot of promise. Many studies have been conducted to improve our understanding of this methodology. Though many has been successful, there have been contradicting results. Further studies have been difficult because the results were obtained using not only different models, but on different platforms as well. Any comparison at this point will be full of uncertainties simply because there are too many factors to consider.

I wish to contribute to the field of artificial development with this thesis. However, there will be no comparisons of various development models, or invention of a new model. I will leave these types tasks to better people. Instead, a platform to build development models will be introduced. The purpose of this platform will be made clear in course of the thesis. Two prominent models will be picked out to be ported to this platform, and it will be shown that not only is it possible to re-implement these models, it is also possible to reproduce the results. This feat will demonstrate the flexibility of the framework as well the benefits of using it.
\end{abstract}
